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. Author manuscript; available in PMC: 2021 Oct 20.
Published in final edited form as: J Neurosci Methods. 2021 Jul 6;361:109282. doi: 10.1016/j.jneumeth.2021.109282

Table 4.

Architecture and parameters of the CNN model

Layer Number of filters Kernel size Stride Output shape Number of parameters Regularization
Input - - - (1000, 27) 0 -
1D convolution 8 9 1 (992, 8) 1952 -
Max-pooling 8 2 2 (496, 8) 0 -
1D convolution 12 9 1 (488, 12) 876 -
Max-pooling 12 2 2 (244, 12) 0 -
1D convolution 12 9 1 (236, 12) 1380 -
Max-pooling 12 2 2 (118, 12) 0 -
1D convolution 16 9 1 (110, 16) 1744 -
Max-pooling 16 2 2 (55, 16) 0 -
Dense - - - (1, 30) 26430 Dropout (0.5)
Dense - - - (1, 5) 155 Dropout (0.5)
Dense - - - (1, 2) 12 -
Total 32477